Papers › MotionMixer: MLP-based 3D Human Body Pose Forecasting
MotionMixer: MLP-based 3D Human Body Pose Forecasting
Arij Bouazizi, Adrian Holzbock, Ulrich Kressel, Klaus Dietmayer, Vasileios Belagiannis
In this work, we present MotionMixer, an efficient 3D human body pose forecasting model based solely on multi-layer perceptrons (MLPs). MotionMixer learns the spatial-temporal 3D body pose dependencies by sequentially mixing both modalities. Given a stacked sequence of 3D body poses, a spatial-MLP extracts fine grained spatial dependencies of the body joints. The interaction of the body joints over time is then modelled by a temporal MLP. The spatial-temporal mixed features are finally aggregated and decoded to obtain the future motion. To calibrate the influence of each time step in the pose sequence, we make use of squeeze-and-excitation (SE) blocks. We evaluate our approach on Human3.6M, AMASS, and 3DPW datasets using the standard evaluation protocols. For all evaluations, we demonstrate state-of-the-art performance, while having a model with a smaller number of parameters. Our code is available at: https://github.com/MotionMLP/MotionMixer
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Human Pose Forecasting | Human3.6M | MotionMixer | Average MPJPE (mm) @ 1000 ms | 111.0 | #11 of 33 | Archive leaderboard | report |
| Human Pose Forecasting | Human3.6M | MotionMixer | Average MPJPE (mm) @ 400ms | 59.3 | #11 of 33 | Archive leaderboard | report |
| Human Pose Forecasting | Human3.6M | MotionMixer | MAR, walking, 1,000ms | 0.73 | #11 of 33 | Archive leaderboard | report |
| Human Pose Forecasting | Human3.6M | MotionMixer | MAR, walking, 400ms | 0.58 | #11 of 33 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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